Recently, ANNs have been also employed to extrapolate the results of ab initio nuclear structure calculations in finite model spaces, which otherwise would be extremely costly from a computational point of view. In particular, Negoita et al. [1]}, [2]} have used a feed-forward ANN method for predicting the ground state energy and the ground state point proton root-mean-squared radius of \(^6\) Li. Training the network with No-Core Shell Model (NCSM) results, obtained in accessible harmonic oscillator (HO) basis spaces for various oscillators spacings \(\hbar \omega \)  and different numbers \(N_{max}\hbar \omega \)  of maximum excitation energies, these authors showed that an ANN is able to predict correctly extrapolations of the NCSM results to very large model spaces of size \(N_{max}\sim 100\) , and that their dependence on \(\hbar \omega \)  vanishes as the value of \(N_{max}\)  increases. Similarly, Jiang et al. [3]} have also employed an ANN to extrapolate the ground state energy and radii of \(^4\) He, \(^6\) Li and \(^{16}\) O computed with the NCSM and the coupled-cluster (CC) methods. These authors have shown that a preprocessing of the input data, and the inclusion of correlations among these data, reduces the problem of multiple solutions yielding more stable results and consistent estimations of the uncertainty.
